import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

with tf.name_scope('define_input'):
    x = tf.placeholder(tf.float32, [None, 784], name='image_input')

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

with tf.variable_scope('output_labels'):
    y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
sess = tf.Session()
sess.run(tf.initialize_all_variables())

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))


#for op in tf.get_default_graph().get_operations(): print(str(op.name))
#for tensor in tf.get_default_graph().as_graph_def().node: print(tensor.name)

graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
output_graph_def = tf.graph_util.convert_variables_to_constants(
    sess=sess,
    input_graph_def=input_graph_def,
    output_node_names=['output_labels/Softmax'])
 
with tf.gfile.GFile('mnist.pb', "wb") as f:
    f.write(output_graph_def.SerializeToString())

sess.close()
